load_image
Load image files or base64-encoded images into the Image Processing MCP Server for subsequent editing, transformation, or analysis operations.
Instructions
加载图片文件或base64编码的图片
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | 图片文件路径或base64编码的图片数据。支持本地文件路径（如 'image.jpg'）或base64编码字符串 |
Implementation Reference
- tools/basic.py:106-153 (handler)Main execution logic for the load_image tool: validates source, loads image using processor, retrieves info, outputs image reference, and returns JSON response.async def load_image(source: str) -> list[TextContent]: """ 加载图片 Args: source: 图片源（文件路径或base64编码） Returns: 包含图片信息和文件引用的响应 """ try: # 验证图片源 ensure_valid_image_source(source) # 加载图片 image = processor.load_image(source) # 获取图片信息 info = processor.get_image_info(image) # 输出图片（文件引用模式） output_info = processor.output_image(image, "loaded") result = { "success": True, "message": "图片加载成功", "data": { **output_info, "info": info } } return [TextContent(type="text", text=json.dumps(result, ensure_ascii=False))] except ValidationError as e: error_result = { "success": False, "error": f"参数验证失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] except Exception as e: error_result = { "success": False, "error": f"图片加载失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
- main.py:107-120 (registration)MCP server tool registration using @mcp.tool() decorator. Defines input schema via Annotated Field and delegates execution to basic_load_image handler.@mcp.tool() def load_image( source: Annotated[str, Field(description="图片文件路径或base64编码的图片数据。支持本地文件路径（如 'image.jpg'）或base64编码字符串")] ) -> str: """加载图片文件或base64编码的图片""" try: result = safe_run_async(basic_load_image(source)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"加载图片失败: {str(e)}" }, ensure_ascii=False, indent=2)
- main.py:109-109 (schema)Input schema definition for the load_image tool using Pydantic Annotated and Field for source parameter validation and description.source: Annotated[str, Field(description="图片文件路径或base64编码的图片数据。支持本地文件路径（如 'image.jpg'）或base64编码字符串")]
- utils/image_processor.py:25-64 (helper)Core utility function in ImageProcessor class that loads image from file path or base64 string into PIL Image object, with size validation.def load_image(self, source: Union[str, bytes]) -> Image.Image: """ 加载图片，支持文件路径、base64编码 Args: source: 图片源，可以是文件路径或base64编码字符串 Returns: PIL Image对象 Raises: ValueError: 当图片源无效时 IOError: 当图片无法加载时 """ try: if isinstance(source, str): if source.startswith('data:image'): # base64编码的图片 header, data = source.split(',', 1) image_data = base64.b64decode(data) image = Image.open(io.BytesIO(image_data)) else: # 文件路径 if not os.path.exists(source): raise ValueError(f"图片文件不存在: {source}") image = Image.open(source) elif isinstance(source, bytes): image = Image.open(io.BytesIO(source)) else: raise ValueError("不支持的图片源类型") # 检查图片尺寸 if image.size[0] > self.max_image_size[0] or image.size[1] > self.max_image_size[1]: raise ValueError(f"图片尺寸过大，最大支持: {self.max_image_size}") return image except Exception as e: raise IOError(f"图片加载失败: {str(e)}")